Solitary
# Load necessary libraries
# Reading and wrangling
library(googlesheets4)
library(readr)
library(tidyverse)
library(janitor)
library(lubridate)
library(DT)
# Plotting
library(ggplot2)
library(RColorBrewer)
# Tables
library(kableExtra)
# Load custom function
source("function_clean_facility_names.R", local = knitr::knit_global())
# Read in Sheet G-324A-19
df_324 <- read_sheet("https://docs.google.com/spreadsheets/d/1im5VSi3bIEi13O8WQ56wEIXSyNEstbGMylXXgD9bAG0/edit#gid=1858227071",
sheet="G-324A-19",
col_names = TRUE,
col_types = "c") %>%
clean_names() %>%
# Run custom cleaning function
clean_facility_names() %>%
# df specific changes
mutate(facility = as.factor(facility),
state = as.factor(state),
date = mdy(inspection_date),
current_inspection_date_from = mdy(current_inspection_date_from),
current_inspection_date_to = mdy(current_inspection_date_to)
) %>%
relocate(date, .before = inspection_date) %>%
mutate_at(c(20:49), as.numeric)
## Reading from "000inspection_forms"
## Range "'G-324A-19'"
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [182].
## Warning: 1 failed to parse.
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
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# Read Google Sheet incident worksheet, convert to data frame, and wrangle
df_324_inc <- read_sheet("https://docs.google.com/spreadsheets/d/1im5VSi3bIEi13O8WQ56wEIXSyNEstbGMylXXgD9bAG0/edit#gid=1858227071",
sheet="G-324A-19-inc",
col_types = "c") %>%
clean_names() %>%
# Run custom cleaning function
clean_facility_names() %>%
# df_specific changes
unite(date, year:month) %>%
mutate(facility = as.factor(facility),
state = as.factor(state),
date = ym(date)
) %>%
mutate_at(c(6:76), as.numeric)
## Reading from "000inspection_forms"
## Range "'G-324A-19-inc'"
## New names:
## * `Sexual abuse allegations detainee on staff/contractor/volunteer` -> `Sexual abuse allegations detainee on staff/contractor/volunteer...29`
## * `Sexual abuse allegations detainee on staff/contractor/volunteer` -> `Sexual abuse allegations detainee on staff/contractor/volunteer...34`
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 12 rows [2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152,
## 2153, 2154, 2155].
## Warning: 1 failed to parse.
## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
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Summary Tables
Of the present 163 inspections reviewed so far, there are more than 34,000 instances of solitary. That is roughly 208 instances of solitary per inspection.
df_solitary <- df_324_inc %>%
# Select a subset of columns to work with
select(id,
facility,
date,
detainees_placed_in_administrative_segregation:
detainees_placed_in_segregation_for_mental_health_reasons) %>%
# Need the rowwise function to compute a row-at-a-time
# in the following mutate function
rowwise(id) %>%
# Create new total column
mutate(total_segregation = sum(c_across(detainees_placed_in_administrative_segregation:
detainees_placed_in_segregation_for_mental_health_reasons))) %>%
# Tidy
pivot_longer(.,
cols= detainees_placed_in_administrative_segregation:
total_segregation,
names_to = "segregation_type",
values_to = "segregation_count") %>%
# Remove NA
drop_na() %>%
# Explicitly set factor levels
mutate(segregation_type = factor(segregation_type, levels = c(
"detainees_placed_in_administrative_segregation",
"detainees_placed_in_disciplinary_segregation",
"detainees_placed_in_segregation_for_medical_reasons",
"detainees_placed_in_segregation_for_mental_health_reasons",
"total_segregation"
)))
df_solitary %>%
group_by(segregation_type) %>%
summarise(`Total Solitary by Type` = sum(segregation_count)) %>%
ungroup() %>%
kable(caption = "Total Solitary by Type",
col.names = c("Solitary Type", "Total Solitary Type")) %>%
kable_styling(c("hover", "striped", "condensed", "responsive"))
Table 1.1: Total Solitary by Type
|
Solitary Type
|
Total Solitary Type
|
|
detainees_placed_in_administrative_segregation
|
17236
|
|
detainees_placed_in_disciplinary_segregation
|
9551
|
|
detainees_placed_in_segregation_for_medical_reasons
|
9869
|
|
detainees_placed_in_segregation_for_mental_health_reasons
|
1003
|
|
total_segregation
|
37336
|
df_solitary %>%
group_by(facility) %>%
summarise(total_segregation = sum(segregation_count)) %>%
arrange(desc(total_segregation)) %>%
ungroup() %>%
kable(caption = "Total Solitary by Facility",
col.names = c("Facility", "Total Solitary by Facility")) %>%
kable_styling(c("hover", "striped", "condensed", "responsive")) %>%
scroll_box(height = "300px")
Table 1.1: Total Solitary by Facility
|
Facility
|
Total Solitary by Facility
|
|
Krome Service Processing Center
|
10882
|
|
Eloy Detention Center
|
4556
|
|
El Paso Service Processing Center
|
3310
|
|
Prairieland Detention Center
|
2788
|
|
Henderson Detention Center
|
2638
|
|
Krome North Service Processing Center
|
2510
|
|
Winn Correctional Center
|
2474
|
|
Caroline Detention Facility
|
2438
|
|
Otero County Processing Center
|
2332
|
|
La Palma Correctional Center
|
2198
|
|
Adelanto ICE Processing Center - West
|
2178
|
|
Irwin County Detention Center
|
2172
|
|
River Correctional Center
|
1898
|
|
Stewart Detention Center
|
1814
|
|
South Texas ICE Processing Center
|
1696
|
|
Adams County Correctional Center
|
1456
|
|
York County Prison
|
1418
|
|
Montgomery Processing Center
|
1324
|
|
Adelanto ICE Processing Center - East
|
1308
|
|
Otay Mesa Detention Center
|
1266
|
|
Jackson Parish Correctional Center
|
1248
|
|
Aurora ICE Processing Center
|
1138
|
|
Aurora ICE Processing Center II - Annex
|
1138
|
|
Northwest ICE Processing Center
|
1028
|
|
Immigration Centers of America - Farmville
|
1006
|
|
Imperial Regional Detention Facility
|
1006
|
|
Glades County Detention Center
|
974
|
|
Port Isabel Service Processing Center
|
932
|
|
LaSalle ICE Processing Center
|
928
|
|
Sherburne County Jail
|
796
|
|
Pulaski County Detention Center
|
688
|
|
Catahoula Correctional Center
|
662
|
|
Pike County Correctional Facility
|
606
|
|
Jena LaSalle Detention Facility
|
602
|
|
Pine Prairie ICE Processing Center
|
554
|
|
Baker County Detention Center
|
468
|
|
McHenry County Adult Correctional Facility
|
462
|
|
Bluebonnet Detention Center
|
452
|
|
Florence Service Processing Center
|
388
|
|
Bergen County Jail
|
372
|
|
Houston Contract Detention Facility
|
372
|
|
Bristol County Jail and House of Correction
|
360
|
|
Wakulla County Detention Facility
|
342
|
|
Polk County Adult Detention Center
|
296
|
|
Calhoun County Correctional Center
|
274
|
|
Freeborn County Adult Detention Center
|
256
|
|
Richwood Correctional Center
|
256
|
|
Yuba County Jail
|
224
|
|
Hudson County Corrections and Rehabilitation Center
|
220
|
|
Essex County Correctional Facility
|
213
|
|
Limestone County Detention Center
|
206
|
|
Clinton County Correctional Facility
|
196
|
|
Donald W. Wyatt Detention Facility
|
194
|
|
Plymouth County Correctional Facility
|
176
|
|
El Valle Detention Facility
|
172
|
|
Dodge County Detention Facility
|
162
|
|
Geauga County Jail
|
150
|
|
Allen Parish Public Safety Complex
|
142
|
|
LaSalle Correctional Center
|
140
|
|
Okmulgee County Jail - Moore Detention Facility
|
140
|
|
Johnson County Corrections Center
|
136
|
|
Saint Clair County Jail
|
134
|
|
Elizabeth Contract Detention Facility
|
132
|
|
Folkston ICE Processing Center
|
126
|
|
Jerome Combs Detention Center
|
124
|
|
Joe Corley Processing Center
|
120
|
|
Nye County Detention Center
|
120
|
|
Mesa Verde ICE Processing Facility
|
108
|
|
Seneca County Jail
|
102
|
|
Kay County Detention Center
|
92
|
|
Worcester County Jail
|
82
|
|
Eden Detention Center
|
78
|
|
Clay County Jail
|
76
|
|
Butler County Jail
|
72
|
|
Torrance County Detention Facility
|
58
|
|
Hardin County Jail
|
56
|
|
Bossier Parish Corrections Center
|
54
|
|
Webb County Detention Center
|
54
|
|
Chippewa County Correctional Facility
|
50
|
|
David L. Moss Criminal Justice Center
|
46
|
|
Cambria County Prison
|
44
|
|
Montgomery County Jail
|
42
|
|
Strafford County Department of Corrections
|
42
|
|
Washoe County Detention Center
|
42
|
|
Hall County Department of Corrections
|
40
|
|
Rio Grande Detention Center
|
36
|
|
CCA Florence Correctional Center
|
34
|
|
Cibola County Correctional Center
|
32
|
|
Boone County Jail
|
28
|
|
Golden State Annex
|
26
|
|
Folkston ICE Processing Center Annex
|
24
|
|
Desert View Annex
|
22
|
|
Laredo Processing Center
|
22
|
|
Cass County Jail
|
20
|
|
Morgan County Adult Detention Center
|
20
|
|
Alamance County Detention Center
|
16
|
|
Teller County Jail
|
16
|
|
Northern Oregon Correctional Facility
|
14
|
|
Sheriff Al Cannon Detention Center
|
14
|
|
Carver County Jail
|
10
|
|
Howard County Detention Center
|
10
|
|
Orange County Correctional Facility
|
8
|
|
Shawnee County Department of Corrections - Adult Detention Center
|
6
|
|
Christian County Jail
|
4
|
|
Rolling Plains Detention Center
|
4
|
|
San Luis Regional Detention Center
|
4
|
|
Brooks County Detention Center
|
0
|
|
Broward Transitional Center
|
0
|
|
Coastal Bend Detention Center
|
0
|
|
Dorchester County Detention Center
|
0
|
|
Douglas County Department of Corrections
|
0
|
|
East Hidalgo Detention Center
|
0
|
|
LaSalle County Regional Detention Center
|
0
|
|
Monroe County Inmate Dormitory
|
0
|
|
Morrow County Correctional Facility
|
0
|
|
Platte County Detention Center
|
0
|
|
Robert A. Deyton Detention Facility
|
0
|
|
South Louisiana ICE Processing Center
|
0
|
|
T. Don Hutto Residential Center
|
0
|
|
Val Verde Correctional Facility
|
0
|
|
West Texas Detention Facility
|
0
|
|
Western Tennessee Detention Facility
|
0
|
|
Willacy County Regional Detention Facility
|
0
|
Facet Plots of Solitary by Facility
# Generating a linetype vector for use in the plot
plot_lines <- c(
"solid",
"solid",
"solid",
"solid",
"dotted"
)
# Use Color Brewer to set colors and modify
# the last color to be black for totals.
plot_colors <- brewer.pal(5, "Paired")
plot_colors[5] <- "#000000"
# Create plot labels
plot_labels <- c(
"Administrative",
"Disciplinary",
"Medical",
"Mental Health",
"Total")
df_solitary %>%
# Calling the plot and formatting
ggplot(aes(x=date,
y = segregation_count,
linetype = segregation_type))+
geom_line(aes(color = segregation_type), size = .65) +
# Set the color
scale_color_manual(
values = plot_colors,
name = "Solitary Type:",
labels = plot_labels)+
# Set the linetype
scale_linetype_manual(
values = plot_lines,
name = "Solitary Type:",
labels = plot_labels)+
labs(title = "Reported Use of Solitary")+
ylab("Number of Individuals Palced in Solitary")+
xlab("Date")+
theme(
strip.text = element_text(size = 8),
legend.position = "bottom"
)+
facet_wrap(~ facility, ncol=3)

Solitary Over 60 Days
# Use Color Brewer to set colors and modify
# the last color to be black for totals.
plot_colors <- brewer.pal(5, "Paired")
plot_colors[5] <- "#000000"
# Call the dataframe and select cols
df_324 %>%
select(id,
facility,
state,
date,
fac_operator,
admin_seg_60_ice,
disc_seg_60_ice) %>%
drop_na() %>%
# Need the rowwise function to compute a row-at-a-time
# in the following mutate function
rowwise(id) %>%
# Generate total col
mutate(total_seg_60 = sum(c_across(
admin_seg_60_ice:
disc_seg_60_ice
))) %>%
# Make tidy and filter
pivot_longer(cols = admin_seg_60_ice:disc_seg_60_ice,
names_to = "segregation_60_type",
values_to = "segregation_60_count") %>%
filter(segregation_60_type %in% c("admin_seg_60_ice", "disc_seg_60_ice")&
segregation_60_count > 0) %>%
# Initiate the plot and sort by sum
ggplot(aes(x = segregation_60_count,
y=reorder(fac_operator, segregation_60_count, sum),
fill=segregation_60_type))+
geom_bar(stat = "identity")+
# Set the color fill
scale_fill_brewer(type = "qual",
palette = "Paired",
name = "Segregation > 60 Type",
labels = c("Administrative",
"Disciplinary"))+
labs(title= "Segregation > 60 Days by Facility Operator",
x = "Segregation > 60 Days Count",
y = "Facility Operator")+
theme(legend.position = "bottom")

---
title: "Solitary"
author: "Nathan Craig"
date: "`r format(Sys.Date(), '%A %B %d, %Y')`"
output:
  bookdown::html_document2: 
    toc: true
    toc_float: true
    number_sections: true
    df_print: paged
    code_folding: hide
    code_download: true
bibliography: [citations.bib, packages.bib]
---
# Solitary


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


```{r load-libraries}
# Load necessary libraries

# Reading and wrangling
library(googlesheets4)
library(readr)
library(tidyverse)
library(janitor)
library(lubridate)
library(DT)

# Plotting
library(ggplot2)
library(RColorBrewer)

# Tables
library(kableExtra)

# Load custom function
source("function_clean_facility_names.R", local = knitr::knit_global())
```


```{r read-data-324}
# Read in Sheet G-324A-19
df_324 <- read_sheet("https://docs.google.com/spreadsheets/d/1im5VSi3bIEi13O8WQ56wEIXSyNEstbGMylXXgD9bAG0/edit#gid=1858227071",
                 sheet="G-324A-19",
                 col_names = TRUE,
                 col_types = "c") %>% 
  clean_names() %>% 
  
  # Run custom cleaning function
  clean_facility_names() %>% 
  
  # df specific changes
  mutate(facility = as.factor(facility),
         state = as.factor(state),
         date = mdy(inspection_date),
         current_inspection_date_from = mdy(current_inspection_date_from),
         current_inspection_date_to = mdy(current_inspection_date_to)
         ) %>% 
  relocate(date, .before = inspection_date) %>% 
  mutate_at(c(20:49), as.numeric)
```


```{r read-data-324-incident}
# Read Google Sheet incident worksheet, convert to data frame, and wrangle
df_324_inc <- read_sheet("https://docs.google.com/spreadsheets/d/1im5VSi3bIEi13O8WQ56wEIXSyNEstbGMylXXgD9bAG0/edit#gid=1858227071",
                 sheet="G-324A-19-inc",
                 col_types = "c") %>% 
  clean_names() %>%
  
  # Run custom cleaning function
  clean_facility_names() %>% 


  # df_specific changes
  unite(date, year:month) %>% 
  mutate(facility = as.factor(facility),
         state = as.factor(state),
         date = ym(date)
         ) %>% 
  mutate_at(c(6:76), as.numeric)
```


## Summary Tables

Of the present 163 inspections reviewed so far, there are more than 34,000 instances of solitary. That is roughly 208 instances of solitary per inspection.

```{r}
df_solitary <- df_324_inc %>%
  
  # Select a subset of columns to work with
  select(id,
         facility,
         date,
         detainees_placed_in_administrative_segregation:
           detainees_placed_in_segregation_for_mental_health_reasons) %>% 
  
  # Need the rowwise function to compute a row-at-a-time
  # in the following mutate function
  rowwise(id) %>% 
  
  # Create new total column
  mutate(total_segregation = sum(c_across(detainees_placed_in_administrative_segregation:
                 detainees_placed_in_segregation_for_mental_health_reasons))) %>% 
  
  # Tidy
  pivot_longer(.,
               cols= detainees_placed_in_administrative_segregation:
                 total_segregation,
               names_to = "segregation_type",
               values_to = "segregation_count") %>% 
  
  # Remove NA
  drop_na() %>% 
  
  # Explicitly set factor levels
  mutate(segregation_type = factor(segregation_type, levels = c(
    "detainees_placed_in_administrative_segregation",
    "detainees_placed_in_disciplinary_segregation",
    "detainees_placed_in_segregation_for_medical_reasons",
    "detainees_placed_in_segregation_for_mental_health_reasons",
    "total_segregation"
  )))
```

```{r}
df_solitary %>% 
  group_by(segregation_type) %>% 
  summarise(`Total Solitary by Type` = sum(segregation_count)) %>% 
  ungroup() %>% 
  kable(caption = "Total Solitary by Type",
        col.names = c("Solitary Type", "Total Solitary Type")) %>% 
  kable_styling(c("hover", "striped", "condensed", "responsive"))


df_solitary %>% 
  group_by(facility) %>% 
  summarise(total_segregation = sum(segregation_count)) %>% 
  arrange(desc(total_segregation)) %>% 
  ungroup() %>% 
  kable(caption = "Total Solitary by Facility",
        col.names = c("Facility", "Total Solitary by Facility")) %>% 
  kable_styling(c("hover", "striped", "condensed", "responsive")) %>% 
  scroll_box(height = "300px")
```

## Facet Plots of Solitary by Facility

```{r solitary-facet, fig.height=40}
# Generating a linetype vector for use in the plot
plot_lines <- c(
    "solid",
    "solid",
    "solid",
    "solid",
    "dotted"
    )

# Use Color Brewer to set colors and modify
# the last color to be black for totals.
plot_colors <- brewer.pal(5, "Paired")
plot_colors[5] <- "#000000"

# Create plot labels
plot_labels <- c(
    "Administrative",
    "Disciplinary",
    "Medical",
    "Mental Health",
    "Total")

df_solitary %>% 

# Calling the plot and formatting
  ggplot(aes(x=date,
             y = segregation_count,
             linetype = segregation_type))+
  geom_line(aes(color = segregation_type), size = .65) +
  
  # Set the color
  scale_color_manual(
  values = plot_colors,
  name = "Solitary Type:",
  labels = plot_labels)+
  
  # Set the linetype
  scale_linetype_manual(
    values = plot_lines,
      name = "Solitary Type:",
    labels = plot_labels)+
  
  
  labs(title = "Reported Use of Solitary")+
  ylab("Number of Individuals Palced in Solitary")+
  xlab("Date")+
  theme(
    strip.text = element_text(size = 8),
    legend.position = "bottom"
    )+
    facet_wrap(~ facility, ncol=3)
```

## Solitary Over 60 Days

```{r solitary-60-facet}

# Use Color Brewer to set colors and modify
# the last color to be black for totals.
plot_colors <- brewer.pal(5, "Paired")
plot_colors[5] <- "#000000"

# Call the dataframe and select cols
df_324 %>% 
  select(id,
         facility,
         state,
         date,
         fac_operator,
         admin_seg_60_ice,
         disc_seg_60_ice) %>% 
  drop_na() %>% 
  
  # Need the rowwise function to compute a row-at-a-time
  # in the following mutate function
  rowwise(id) %>% 
  
  # Generate total col
  mutate(total_seg_60 = sum(c_across(
       admin_seg_60_ice:
       disc_seg_60_ice
  ))) %>%
  
  # Make tidy and filter
  pivot_longer(cols = admin_seg_60_ice:disc_seg_60_ice,
               names_to = "segregation_60_type",
               values_to = "segregation_60_count") %>%
  filter(segregation_60_type %in% c("admin_seg_60_ice", "disc_seg_60_ice")&
           segregation_60_count > 0) %>%
  
  # Initiate the plot and sort by sum
  ggplot(aes(x = segregation_60_count,
             y=reorder(fac_operator, segregation_60_count, sum),
             fill=segregation_60_type))+
  geom_bar(stat = "identity")+
  
  # Set the color fill
  scale_fill_brewer(type = "qual",
                    palette = "Paired",
                    name = "Segregation > 60 Type",
                    labels = c("Administrative",
                               "Disciplinary"))+
  labs(title= "Segregation > 60 Days by Facility Operator",
        x = "Segregation > 60 Days Count",
        y = "Facility Operator")+
  theme(legend.position = "bottom")
```


